EvoVis: Dashboard for Visualizing Evolutionary Neural Architecture Search Algorithms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
EvoVis is a dashboard designed to visualize the key components of Evolutionary Neural Architecture Search (ENAS) algorithms.ENAS is an optimization method that mimics biological evolution to optimize one or multiple objectives, ultimately discovering novel neural network architectures tailored to specific tasks.EvoVis offers a holistic view of the ENAS process: It provides insights into hyperparameters, potential neural architecture topologies, the family tree of architectures across generations, and performance trends.Key features include interactive gene pool and family tree graphs, as well as performance plots for monitoring the ENAS runs.The dashboard's generic data structure interface facilitates integration with various ENAS algorithms.The code is available at https://github.com/ankilab/EvoVis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it